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st assays measure gene expression  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc st assays measure gene expression
    St Assays Measure Gene Expression, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/spatial+transcriptomics+st/assays+expression+gene+measure+st/pmc12707688-20-2-0
    Average 86 stars, based on 1 article reviews
    st assays measure gene expression - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
    Article Snippet: .. Spatial transcriptomics (ST) assays measure gene expression at thousands to hundreds-of-thousands of spatial locations within tissue sections, enabling researchers to characterize transcript abundance in vivo at resolutions ranging from small cell groups to subcellular structures [ , ]. ..

    Gene Expression:

    Article Title: Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
    Article Snippet: .. Spatial transcriptomics (ST) assays measure gene expression at thousands to hundreds-of-thousands of spatial locations within tissue sections, enabling researchers to characterize transcript abundance in vivo at resolutions ranging from small cell groups to subcellular structures [ , ]. ..

    In Vivo:

    Article Title: Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
    Article Snippet: .. Spatial transcriptomics (ST) assays measure gene expression at thousands to hundreds-of-thousands of spatial locations within tissue sections, enabling researchers to characterize transcript abundance in vivo at resolutions ranging from small cell groups to subcellular structures [ , ]. ..



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    Spatial organization and cell-cell communication networks in the tumor microenvironment. (A–C) The developmental trajectories of cell sub-populations from a spatial perspective are investigated. (D, E) Heatmap and network diagrams displaying cell–cell dependency analysis in the colocated, neighboring, and extended neighboring (15-point) regions of the spatial transcriptomics data. (F) The interaction heatmap visualized the intensity of intercellular interactions mediated by the ligand-receptor pairs. (G) The spatial cell communication network diagram illustrates that NUhighepi exhibit a higher intensity of cell communication with other cells. (H) Circos plot summarizing cell-type-specific interaction patterns.

    Journal: Frontiers in Oncology

    Article Title: Spatial transcriptome and single-cell sequencing reveal the role of nucleotide metabolism in breast cancer progression and tumor microenvironment

    doi: 10.3389/fonc.2025.1703778

    Figure Lengend Snippet: Spatial organization and cell-cell communication networks in the tumor microenvironment. (A–C) The developmental trajectories of cell sub-populations from a spatial perspective are investigated. (D, E) Heatmap and network diagrams displaying cell–cell dependency analysis in the colocated, neighboring, and extended neighboring (15-point) regions of the spatial transcriptomics data. (F) The interaction heatmap visualized the intensity of intercellular interactions mediated by the ligand-receptor pairs. (G) The spatial cell communication network diagram illustrates that NUhighepi exhibit a higher intensity of cell communication with other cells. (H) Circos plot summarizing cell-type-specific interaction patterns.

    Article Snippet: Breast cancer spatial transcriptomics (ST) data were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) and 10x Genomics official website ( https://www.10xgenomics.com/ ).

    Techniques:

    Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques:

    Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques: Expressing

    High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques: Expressing, Membrane